Bayesian approaches to benefit-risk assessment for diagnostic tests.

Benefit-risk assessment plays an important role in the evaluation of medical devices. Unlike the therapeutic devices, the diagnostic tests usually affect patient life indirectly since subsequent therapeutic treatment interventions (such as proper treatment in time, further examination or test, no action, etc.) will depend on correct diagnosis and monitoring of the disease status. A benefit-risk score using statistical models by integrating the information from benefit (true positive and true negative) and risk (false positive and false negative) for diagnostic tests with binary outcomes (i.e., positive and negative) will help evaluation of the utility and the uncertainty of a particular diagnostic device. In this paper, we develop two types of Bayesian models with conjugate priors for constructing the benefit-risk (BR) measures with corresponding credible intervals, one based on a Multinomial model with Dirichlet prior, and the other based on independent Binomial models with independent Beta priors. We then propose a Bayesian power prior model to incorporate the historical data or the real-world data (RWD). Both the fixed and random power prior parameters are considered for Bayesian borrowing. We evaluate the performance of the methods by simulations and illustrate their implementation using a real example.